Fast asynchronous byzantine agreement and leader election with full information
Bibliographic record
Abstract
We resolve two long-standing open problems in distributed computation by showing that both Byzantine agreement and Leader Election can be solved in sub-exponential time in the asynchronous full information model. Surprisingly, our protocols for both problems run in only polylogarithmic time. We thus achieve a better than exponential speedup over previous results for asynchronous Byzantine agreement. In addition, to the best of our knowledge, ours is the first protocol for asynchronous full-information leader election. Our protocols work in the full information model with a non-adaptive adversary: the adversary is assumed to control up to a constant fraction of the processors, have unlimited computational power as well as access to all communications, but no access to processors ’ private random bits. The adversary is non-adaptive only in the sense that the corrupted processors must be chosen at the outset. Our protocols run in time that is polylogarithmic in the number of processors, n, and tolerate t < n 6+ɛ faulty processors for any positive constant ɛ. Our protocols are Monte Carlo, succeeding with probability 1 − o(1) for Byzantine agreement, and constant probability for leader election.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".